Hi Everybody 👋🏻

Description:

Objective:

Countries can be categorized based on socioeconomic and health indicators that measure a nation's overall level of development. Key factors include economic prosperity, education, healthcare access, and quality of life. By analyzing metrics in areas like income, literacy, life expectancy, and standards of living, countries can be grouped and compared in terms of their developmental progress. This allows for identifying development gaps and priorities for improvement across different country income levels and regions. Examining socioeconomic and health criteria provides insights into a nation's human development capacity and areas requiring policy attention.

Problem:

HELP International is a dedicated international humanitarian NGO focused on combating poverty and ensuring access to essential resources and relief in underprivileged nations, particularly in times of disasters and natural calamities. Our organization actively implements various operational projects and engages in advocacy efforts to increase awareness and secure funding for our cause.

Goal:

HELP International has raised $10 million that its CEO now needs to strategically allocate to countries most in need of aid. As a data scientist, my job is to analyze key socioeconomic and health factors to categorize countries by level of development. This will allow me to identify countries facing the biggest development challenges and requiring the most attention and aid money. By examining metrics like income distribution, poverty rates, education levels, and healthcare access and outcomes, I can determine where people lack basic necessities and opportunities. My analysis aims to highlight countries with the lowest scores on human development indexes so the CEO can focus aid efforts on those nations facing the most pressing and profound development needs. Effective use of data on socioeconomic and health disparities will allow me to advise on directing the aid money in a way that maximizes positive impact on vulnerable populations worldwide.

Table Of Content

Table of Contents

  • Step 1 | Nessesary Libraries
    • 1.1 | Import Libraries
    • 1.2 | Library configurations
  • Step 2 | Dataset
    • 2.1 | Load dataset
  • Step 3 | Preprocessing
    • 3.1 | Missing Values
    • 3.2 | Duplicated values
    • 3.3 | Derived Metrices
    • 3.4 | Plots
    • 3.5 | Outliers
    • 3.6 | Normalization
  • Step 4 | PCA
    • 4.1 | Initializing PCA
  • Step 5 | Clustring Models
    • 5.1 | Kmeans
    • 5.2 | KMeans n_clusters=3
  • Author

Step 1 | Nessasary Libraries

📍If you don't have libraries, then you should install them. (ex. pip install pandas)

Step 1.1 | Import Libraries

In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

from sklearn.preprocessing import MinMaxScaler
from sklearn import metrics
from sklearn.decomposition import PCA, IncrementalPCA
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score, calinski_harabasz_score, davies_bouldin_score
from kneed import KneeLocator

import warnings

Step 1.2 | Library configurations

In [2]:
warnings.filterwarnings('ignore')
sns.set_style('whitegrid')
plt.style.use('ggplot')

Step 2 | Dataset

Step 2.1 | Load dataset

📍Here, we import data from a csv file, using pandas library.
In [3]:
data = pd.read_csv('Country-data.csv')
df = pd.DataFrame(data)
df
Out[3]:
country child_mort exports health imports income inflation life_expec total_fer gdpp
0 Afghanistan 90.2 10.0 7.58 44.9 1610 9.44 56.2 5.82 553
1 Albania 16.6 28.0 6.55 48.6 9930 4.49 76.3 1.65 4090
2 Algeria 27.3 38.4 4.17 31.4 12900 16.10 76.5 2.89 4460
3 Angola 119.0 62.3 2.85 42.9 5900 22.40 60.1 6.16 3530
4 Antigua and Barbuda 10.3 45.5 6.03 58.9 19100 1.44 76.8 2.13 12200
... ... ... ... ... ... ... ... ... ... ...
162 Vanuatu 29.2 46.6 5.25 52.7 2950 2.62 63.0 3.50 2970
163 Venezuela 17.1 28.5 4.91 17.6 16500 45.90 75.4 2.47 13500
164 Vietnam 23.3 72.0 6.84 80.2 4490 12.10 73.1 1.95 1310
165 Yemen 56.3 30.0 5.18 34.4 4480 23.60 67.5 4.67 1310
166 Zambia 83.1 37.0 5.89 30.9 3280 14.00 52.0 5.40 1460

167 rows × 10 columns

📍.shape returns the row and column count of the DataFrame in a simple tuple format. 📍.head returns a subset of rows from the top of the DataFrame, allowing convenient previewing of the start of the data. 📍.infoprovides a succinct summary of a DataFrame. It gives concise information about column names, data types, non-missing values and memory usage. 📍.describe(include='all') provides a high-level summary of numeric and object columns in a DataFrame.
In [4]:
df.shape
Out[4]:
(167, 10)
In [5]:
df.head()
Out[5]:
country child_mort exports health imports income inflation life_expec total_fer gdpp
0 Afghanistan 90.2 10.0 7.58 44.9 1610 9.44 56.2 5.82 553
1 Albania 16.6 28.0 6.55 48.6 9930 4.49 76.3 1.65 4090
2 Algeria 27.3 38.4 4.17 31.4 12900 16.10 76.5 2.89 4460
3 Angola 119.0 62.3 2.85 42.9 5900 22.40 60.1 6.16 3530
4 Antigua and Barbuda 10.3 45.5 6.03 58.9 19100 1.44 76.8 2.13 12200
In [6]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 167 entries, 0 to 166
Data columns (total 10 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   country     167 non-null    object 
 1   child_mort  167 non-null    float64
 2   exports     167 non-null    float64
 3   health      167 non-null    float64
 4   imports     167 non-null    float64
 5   income      167 non-null    int64  
 6   inflation   167 non-null    float64
 7   life_expec  167 non-null    float64
 8   total_fer   167 non-null    float64
 9   gdpp        167 non-null    int64  
dtypes: float64(7), int64(2), object(1)
memory usage: 13.2+ KB
📍There is 167 samples, with no-null values. ✅
In [7]:
df.describe(include='all')
Out[7]:
country child_mort exports health imports income inflation life_expec total_fer gdpp
count 167 167.000000 167.000000 167.000000 167.000000 167.000000 167.000000 167.000000 167.000000 167.000000
unique 167 NaN NaN NaN NaN NaN NaN NaN NaN NaN
top Afghanistan NaN NaN NaN NaN NaN NaN NaN NaN NaN
freq 1 NaN NaN NaN NaN NaN NaN NaN NaN NaN
mean NaN 38.270060 41.108976 6.815689 46.890215 17144.688623 7.781832 70.555689 2.947964 12964.155689
std NaN 40.328931 27.412010 2.746837 24.209589 19278.067698 10.570704 8.893172 1.513848 18328.704809
min NaN 2.600000 0.109000 1.810000 0.065900 609.000000 -4.210000 32.100000 1.150000 231.000000
25% NaN 8.250000 23.800000 4.920000 30.200000 3355.000000 1.810000 65.300000 1.795000 1330.000000
50% NaN 19.300000 35.000000 6.320000 43.300000 9960.000000 5.390000 73.100000 2.410000 4660.000000
75% NaN 62.100000 51.350000 8.600000 58.750000 22800.000000 10.750000 76.800000 3.880000 14050.000000
max NaN 208.000000 200.000000 17.900000 174.000000 125000.000000 104.000000 82.800000 7.490000 105000.000000

Step 3 | Preprocessing

Step 3.1 | Missing values

In [8]:
df.isnull().sum()
Out[8]:
country       0
child_mort    0
exports       0
health        0
imports       0
income        0
inflation     0
life_expec    0
total_fer     0
gdpp          0
dtype: int64
📍There is no Missing values in Dataframe. ✅

Step 3.2 | Duplicated values

In [9]:
df_duplicated = df.copy()
df_duplicated.drop_duplicates(subset=None, inplace=True)

df_duplicated.shape
Out[9]:
(167, 10)
In [10]:
df.shape
Out[10]:
(167, 10)
📍There is no duplicated values in Dataframe. ✅

Step 3.3 | Derived Metrices

📍To gain a more accurate understanding of a country's development and economic performance, it is essential to move beyond percentage values and consider actual values of key indicators such as imports, exports, and health spending in relation to their GDP per capita. Relying solely on percentage values can be misleading, as it fails to account for the significant disparities that may exist between countries. For instance, comparing the export percentages of Austria and Belarus might suggest a similar level of economic activity, but their stark differences in GDP per capita reveal a substantial gap in their overall development. By examining the actual values of these indicators, a clearer and more comprehensive picture of a country's economic strength and well-being can be obtained.
In [11]:
df[df['country'] == 'Belarus']
Out[11]:
country child_mort exports health imports income inflation life_expec total_fer gdpp
14 Belarus 5.5 51.4 5.61 64.5 16200 15.1 70.4 1.49 6030
In [12]:
df[df['country'] == 'Austria']
Out[12]:
country child_mort exports health imports income inflation life_expec total_fer gdpp
8 Austria 4.3 51.3 11.0 47.8 43200 0.873 80.5 1.44 46900
In [13]:
df['exports'] = df['exports'] * df['gdpp']/100
df['imports'] = df['imports'] * df['gdpp']/100
df['health'] = df['health'] * df['gdpp']/100
In [14]:
df.head()
Out[14]:
country child_mort exports health imports income inflation life_expec total_fer gdpp
0 Afghanistan 90.2 55.30 41.9174 248.297 1610 9.44 56.2 5.82 553
1 Albania 16.6 1145.20 267.8950 1987.740 9930 4.49 76.3 1.65 4090
2 Algeria 27.3 1712.64 185.9820 1400.440 12900 16.10 76.5 2.89 4460
3 Angola 119.0 2199.19 100.6050 1514.370 5900 22.40 60.1 6.16 3530
4 Antigua and Barbuda 10.3 5551.00 735.6600 7185.800 19100 1.44 76.8 2.13 12200
In [15]:
df.describe()
Out[15]:
child_mort exports health imports income inflation life_expec total_fer gdpp
count 167.000000 167.000000 167.000000 167.000000 167.000000 167.000000 167.000000 167.000000 167.000000
mean 38.270060 7420.618847 1056.733204 6588.352108 17144.688623 7.781832 70.555689 2.947964 12964.155689
std 40.328931 17973.885795 1801.408906 14710.810418 19278.067698 10.570704 8.893172 1.513848 18328.704809
min 2.600000 1.076920 12.821200 0.651092 609.000000 -4.210000 32.100000 1.150000 231.000000
25% 8.250000 447.140000 78.535500 640.215000 3355.000000 1.810000 65.300000 1.795000 1330.000000
50% 19.300000 1777.440000 321.886000 2045.580000 9960.000000 5.390000 73.100000 2.410000 4660.000000
75% 62.100000 7278.000000 976.940000 7719.600000 22800.000000 10.750000 76.800000 3.880000 14050.000000
max 208.000000 183750.000000 8663.600000 149100.000000 125000.000000 104.000000 82.800000 7.490000 105000.000000

Step 3.3 | Plots

In [16]:
df.hist(bins=100,figsize=(30,20))
plt.show()
No description has been provided for this image
In [17]:
plt.figure(figsize=(9, 7))
sns.heatmap(data=df.iloc[:, 1:].corr(), annot=True, fmt=".2f", linewidth=0.75, cmap='Reds')
plt.show()
No description has been provided for this image
📍The country column is not being considered when creating the density plot, since that column is not used to generate the density visualization.✅
In [18]:
density_features=df.columns[1:]
density_features
Out[18]:
Index(['child_mort', 'exports', 'health', 'imports', 'income', 'inflation',
       'life_expec', 'total_fer', 'gdpp'],
      dtype='object')
In [19]:
for i in enumerate(density_features):
    print(i)

(0, 'child_mort')
(1, 'exports')
(2, 'health')
(3, 'imports')
(4, 'income')
(5, 'inflation')
(6, 'life_expec')
(7, 'total_fer')
(8, 'gdpp')
📍 Visusalization of density_features with histplot(data, stat='density'), kde=True to find data distribiution.✅
In [20]:
plt.figure(figsize=(22, 20))
for i, feature in enumerate(density_features):
    plt.subplot(5, 2, i+1)
    sns.histplot(df[feature], stat="density", kde=True)
    plt.title(f"Distribution of {feature}")
plt.tight_layout()
plt.show()
No description has been provided for this image
In [21]:
child_mort = df[['country','child_mort']].sort_values('child_mort', ascending=False)

fig = px.bar(child_mort, x='country', y='child_mort', color='country', labels={'country': 'Country Name', 'child_mort': 'Child Mortality Rate'})
fig.update_layout(title = 'Death of children under 5 years of age per 1000 live births',
                      title_x=0.4,
                      xaxis={'tickangle': 270},
                      width=1000, height=600)
fig.show()
In [22]:
export = df[['country','exports']].sort_values('exports', ascending=False)

fig = px.bar(export, x='country', y='exports', color='country', labels={'country': 'Country Name', 'exports': 'Export'})
fig.update_layout(title = 'Exports of goods and services',
                      title_x=0.4,
                      xaxis={'tickangle': 270},
                      width=1000, height=600)
fig.show()
In [23]:
health = df[['country','health']].sort_values('health', ascending=False)

fig = px.bar(health, x='country', y='health', color='country', labels={'country': 'Country Name', 'health': 'Health'})
fig.update_layout(title = 'Total health spending',
                    title_x=0.4,
                    xaxis={'tickangle': 270},
                    width=1000, height=600)
fig.show()
In [24]:
imports = df[['country','health']].sort_values('health', ascending=False)

fig = px.bar(health, x='country', y='health', color='country', labels={'country': 'Country Name', 'health': 'Health'})
fig.update_layout(title = 'Total health spending',
                    title_x=0.4,
                    xaxis={'tickangle': 270},
                    width=1000, height=600)
fig.show()

Step 3.5 | Outliers

In [25]:
plt.figure(figsize=(24,20))
for i in enumerate(density_features):
    plt.subplot(5,2,i[0]+1)
    sns.boxplot(x= i[1], data = df)
No description has been provided for this image

📍Based on the .boxplot information, we can make the following inferences and outline the required outlier treatment:

1️⃣ Child Mortality: There are outliers on the higher end, indicating countries with high child mortality rates. These countries are the potential targets for aid, so no outlier treatment is necessary. 2️⃣ Exports: There are many outliers on the higher end. To address this, we can cap the outliers at the 95th percentile value. 3️⃣ Health: Similarly, there are outliers on the higher end for health expenditure. Capping these outliers at the 95th percentile value would be appropriate. 4️⃣ Imports: Many countries heavily depend on imports, resulting in outliers on the higher end. Capping these outliers at the 95th percentile value would be suitable. 5️⃣ Income: There are numerous outliers on the higher end for income. Since the focus is on identifying low-income countries for aid, capping the outliers at the 95th percentile value is recommended. 6️⃣ Inflation: Outliers in the higher range of inflation exist. To address this, capping the outliers at the 95th percentile value would be appropriate. 7️⃣ Life Expectancy: There are some outliers on the lower end for life expectancy. Capping these outliers at the 5th percentile value is recommended. 8️⃣ Total Fertility: There is a single outlier on the higher end for total fertility. Capping this outlier at the 95th percentile value would be suitable. 9️⃣ GDP per capita (GDPP): Many outliers exist on the higher end for GDPP. Since the goal is to identify countries with low GDPP for aid, capping the outliers at the 95th percentile value is advised. ⚠️ Considering the limited data available, it is not advisable to remove or drop rows as it may lead to the exclusion of important countries in need of aid. Therefore, capping the outliers as described above would be a more appropriate approach.
In [26]:
# calculate the values of 99th percentile for exports, health, imports, income, inflation, total_fer, gdpp
q4_exports= df['exports'].quantile(.95)
q4_imports= df['imports'].quantile(.95)
q4_health= df['health'].quantile(.95)
q4_income= df['income'].quantile(.95)
q4_inflation= df['inflation'].quantile(.95)
q4_total_fer= df['total_fer'].quantile(.95)
q4_gdpp= df['gdpp'].quantile(.95)

# calculate the values of 1st percentile for life_expec
q1_life_expec= df['life_expec'].quantile(.05)
In [27]:
#perform Outlier capping
df['exports'][df['exports']>= q4_exports] = q4_exports
df['imports'][df['imports']>= q4_imports] = q4_imports
df['health'][df['health']>= q4_health] = q4_health
df['income'][df['income']>= q4_income] = q4_income
df['inflation'][df['inflation']>= q4_inflation] = q4_inflation
df['total_fer'][df['total_fer']>= q4_total_fer] = q4_total_fer
df['gdpp'][df['gdpp']>= q4_gdpp] = q4_gdpp

df['life_expec'][df['life_expec']<= q1_life_expec] = q1_life_expec
📍 Rechecking the .boxplot information to determine if outliers have been removed or not.
In [28]:
plt.figure(figsize=(24,20))
for i in enumerate(density_features):
    plt.subplot(5,2,i[0]+1)
    sns.boxplot(x= i[1], data = df)
No description has been provided for this image
In [29]:
df.describe()
Out[29]:
child_mort exports health imports income inflation life_expec total_fer gdpp
count 167.000000 167.000000 167.000000 167.000000 167.000000 167.000000 167.000000 167.000000 167.000000
mean 38.270060 5783.114656 954.012480 5140.089474 15738.760479 6.929317 70.863593 2.917479 11998.826347
std 40.328931 8580.053847 1453.426636 6696.210005 14787.154215 6.384357 8.085376 1.443771 15158.213199
min 2.600000 1.076920 12.821200 0.651092 609.000000 -4.210000 55.780000 1.150000 231.000000
25% 8.250000 447.140000 78.535500 640.215000 3355.000000 1.810000 65.300000 1.795000 1330.000000
50% 19.300000 1777.440000 321.886000 2045.580000 9960.000000 5.390000 73.100000 2.410000 4660.000000
75% 62.100000 7278.000000 976.940000 7719.600000 22800.000000 10.750000 76.800000 3.880000 14050.000000
max 208.000000 31385.100000 4966.701000 24241.560000 48290.000000 20.870000 82.800000 5.861000 48610.000000

Step 3.5 | Normalization

📍 MinMaxScaler() is a technique used to transform data that falls within a specific range, typically between 0 and 1. This normalization process helps reduce the impact of large values in the dataset.
In [30]:
country = df.country
df.drop(columns='country', inplace=True)
# Normalize
scaler = MinMaxScaler().fit_transform(df)
norm_df = pd.DataFrame(scaler, columns=df.columns)

Step 4 | PCA

📍 PCA (Principal Component Analysis) is a technique that reduces the number of features in a dataset while preserving important patterns. It achieves this by transforming the original features into a new set of uncorrelated variables called principal components. This simplification improves computational efficiency, reduces noise, and aids in data visualization and interpretation.

Step 4.1 | Initializing PCA

In [31]:
pca = PCA(n_components=9).fit(norm_df)
exp = pca.explained_variance_ratio_
print(exp)

[0.70672198 0.13776226 0.07858595 0.03255028 0.01867945 0.01325302
 0.00823535 0.00279766 0.00141406]
In [32]:
plt.plot(np.cumsum(exp), linewidth=2, marker = 'o', linestyle = '--')
plt.title("PCA", fontsize=20)
plt.xlabel('n_component')
plt.ylabel('Variance Ratio')
plt.yticks(np.arange(0.55, 1.05, 0.05))
plt.show()
No description has been provided for this image
📍 n_component=5, has above 95% of result.
In [33]:
final_pca = IncrementalPCA(n_components=5).fit_transform(norm_df)
In [34]:
final_pca.shape
Out[34]:
(167, 5)
In [35]:
pc = np.transpose(final_pca)
In [36]:
corr_pca = np.corrcoef(pc)
In [37]:
sns.heatmap(data=corr_pca, annot=True, fmt=".2f", linewidth=0.75, cmap="Reds")
plt.show()
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In [38]:
pca_df = pd.DataFrame({
    'PC1':pc[0],
    'PC2':pc[1],
    'PC3':pc[2],
    'PC4':pc[3],
    'PC5':pc[4],
})
pca_df
Out[38]:
PC1 PC2 PC3 PC4 PC5
0 -0.868026 0.468154 -0.116122 0.015084 0.030630
1 -0.075952 -0.471567 -0.026781 -0.007173 0.019178
2 -0.214388 -0.201541 0.392519 0.091286 0.133112
3 -0.807603 0.610289 0.331504 0.069905 0.046915
4 0.231294 -0.264191 -0.118967 -0.066490 0.102013
... ... ... ... ... ...
162 -0.453124 -0.050333 -0.243497 -0.067826 -0.040117
163 -0.038596 -0.112832 0.580115 0.142696 0.001215
164 -0.289604 -0.367720 0.210735 0.004976 -0.049341
165 -0.641633 0.186166 0.429498 0.124173 0.116803
166 -0.832937 0.457908 0.070357 0.016176 -0.056084

167 rows × 5 columns

📍Found outliers of each PCA.
In [39]:
fig, ax = plt.subplots(figsize=(15,6))
sns.boxplot(data=pca_df)
plt.show()
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Step 5 | Clustring Model

📍Find best n_clusters for .KMeans .

Step 5.1 | KMeans

In [40]:
kmeans_list = []
kmeans_sil_coef = []
kmeans_calinski_score = []
kmeans_calinski_score = []
kmeans_davies_score = []

for n in range(2, 10) :
    
    # main algorithm
    kmeans = KMeans(n_clusters=n).fit(df)
    kmeans_list.append(kmeans.inertia_)
    
    # Silhouette
    sil_score = silhouette_score(df, kmeans.labels_)
    kmeans_sil_coef.append(sil_score)
    
    # Calinski Harabasz Score
    calinski_score = calinski_harabasz_score(df, kmeans.labels_)
    kmeans_calinski_score.append(calinski_score)

    # Davies Bouldin Score
    davies_score = davies_bouldin_score(df, kmeans.labels_)
    kmeans_davies_score.append(davies_score)



k1 = KneeLocator(range(2, 10), kmeans_list, curve='convex', direction='decreasing')
In [41]:
fig, ax = plt.subplots(2, 2, figsize=(15, 10))

# Ax 1 :  inertia
ax[0, 0].plot(range(2, 10), kmeans_list, label='Inertia', linewidth=2)
ax[0, 0].axvline(x=k1.elbow, ls='--', label='k1 elbow', color='purple', alpha=0.65)
ax[0, 0].set_title('Inertia', fontsize=20, fontweight=600, color='Green')
ax[0, 0].legend()

# Ax 2 : Silhouette Score
ax[0, 1].plot(range(2, 10), kmeans_sil_coef, label='Silhouette Score')
ax[0, 1].set_title('Silhouette Score', fontsize=20, fontweight=600, color='Green')
ax[0, 1].axvline(x=3, ls='--', alpha=0.65, label='Best n', color='Blue')
ax[0, 1].legend()

# Ax 3 : Calinski Harabasz Score
ax[1, 0].plot(range(2, 10),  kmeans_calinski_score, label='Kalinski Harabasz Score')
ax[1, 0].set_title('Kalinski Harabasz Score', fontsize=20, fontweight=600, color='Green')
ax[1, 0].axvline(x=2, ls='--', label='Best n', color='blue', alpha=0.65)
ax[1, 0].axvline(x=3, ls='--', label='Best 2 n', color='purple', alpha=0.65)
ax[1, 0].legend()

# Ax 4 : Davies Bouldin Score
ax[1, 1].plot(range(2, 10), kmeans_davies_score, label='Davies Bouldin Score')
ax[1, 1].set_title('Davies Bouldin Score', fontsize=50, fontweight=700, color='Green')
ax[1, 1].axvline(x=4, ls='--', alpha=0.65, label='Best n', color='Blue')
ax[1, 1].axvline(x=2, ls='--', alpha=0.65, label='second Best', color='purple')
ax[1, 1].set_title('Davies Bouldin Score')
ax[1, 1].legend()


plt.show()
No description has been provided for this image
📍According to the above plots, the best n_clusters for KMeans algorithm is 3.

Step 5.1 | KMeans n_clusters=3

In [42]:
kmeans = KMeans(n_clusters=3).fit(pca_df)
In [43]:
# add country column
pca_df.insert(0, 'Country', country)
pca_df['class'] = kmeans.labels_
In [44]:
pca_df['Requirement'] = pca_df['class']
In [45]:
rich = int(pca_df[pca_df.Country=='Canada']['class'])
middle = int(pca_df[pca_df.Country=='Iran']['class'])
poor = int(pca_df[pca_df.Country=='Afghanistan']['class'])
In [46]:
rich_label = 'Rich countries'
middle_label = 'Middle countries'
poor_label = 'Poor countries'
In [47]:
pca_df.replace({'Requirement':{rich:rich_label, middle:middle_label, poor:poor_label }},inplace=True)
In [48]:
pca_df.head()
Out[48]:
Country PC1 PC2 PC3 PC4 PC5 class Requirement
0 Afghanistan -0.868026 0.468154 -0.116122 0.015084 0.030630 2 Poor countries
1 Albania -0.075952 -0.471567 -0.026781 -0.007173 0.019178 0 Middle countries
2 Algeria -0.214388 -0.201541 0.392519 0.091286 0.133112 0 Middle countries
3 Angola -0.807603 0.610289 0.331504 0.069905 0.046915 2 Poor countries
4 Antigua and Barbuda 0.231294 -0.264191 -0.118967 -0.066490 0.102013 0 Middle countries
In [49]:
fig = px.choropleth(pca_df[['Country','class']],
                    locationmode = 'country names',
                    locations = 'Country',
                    color = pca_df['Requirement'],  
                    color_discrete_map = {'Rich countries': 'Green',
                                          'Middle countries':'LightBlue',
                                          'Poor countries':'Red'}
                   )

fig.update_layout(
    title='World Requirement by Country', 
    title_font_size=24,
    title_x=0.26,
        margin = dict(
                l=10,
                r=0,
                b=0,
                t=50,
                pad=5,
            ),
    )
fig.show()
In [50]:
req_counts = pca_df['Requirement'].value_counts()
fig = px.pie(pca_df, names=req_counts.index, values=req_counts.values,
              hole=0.2, width=1100, height=500
            )
fig.update_layout(title='Country Requirement Distribution', title_x=0.45, title_font=dict(size=24))

fig.show()

Author: Mohammadaref Ahmadpoor

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